In the field of computer vision and computational photography, noise reduction is the application in which granular discrepancies found in images are removed.
Many modern and popular consumer based cameras in digital photography deal with the issues of low-light noise. The issue of low-light noise for a particular digital camera is so important that it is used as a valuable metric of the camera sensor and for determining how well the camera performs. It is for that reason that the problem of low-light image noise reduction is studied and has led to a variety of different methods.
In general, most of the performance evaluations for these various noise reduction methods are performed on small images contaminated with artificial noise (Gaussian, Poisson, salt and pepper, etc.), which is artificially added to a clean image to obtain a noisy version of the image. Measuring the performance of a noise reduction algorithm on small images corrupted by artificial noise might not give an accurate enough picture of the denoising performance of the algorithm on real digital camera or mobile images in low-light conditions.
Accordingly, what is needed in the art is a system and method for generating a dataset of images that is better suited for the analysis of various denoising methods. In particular, the present invention provides a system and method for generating a database of images corrupted by real camera noise.